Control-Aware Radio Resource Allocation for Wireless Estimation Using Lyapunov-Based Priority Scheduling

Abstract

This work studies uplink radio resource allocation for wireless state estimation in a networked control loop, where multiple sensors share an IEEE 802.11ax UL-OFDMA channel. Our communication model accounts for discrete resource unit allocation, a finite set of MCS and transmit-power choices, and packet success probabilities obtained from an EESM based PHY abstraction. In this setup, the transmission time—given by the PPDU duration is dictated by the slowest scheduled user. On the control side, we derive a control-aware value of information (VOI) metric from the LQR induced Lyapunov drift. This VOI quantifies the weighted reduction of the Kalman error covariance that results from a successful packet delivery. Combining these ingredients, we formulate a per cycle utility function that trades off the expected VOI against the added latency and transmit-power costs, all while satisfying UL-OFDMA feasibility constraints. We then propose a greedy scheduler enhanced with a no starvation rule. To keep the computational overhead manageable, the scheduler is implemented with a lazy branch-and-bound scheme that reduces the number of per cycle utility evaluations without altering the selection objective. Simulations on an unstable flexible beam benchmark under Rayleigh fading demonstrate that our Lyapunov priority scheduler achieves a lower average LQG stage cost than a Round Robin baseline, even when both policies use the same RU granularity, power budget, and channel realizations. These results underscore the advantage of coupling physical layer decisions with control driven estimation priorities.